HiPPo: Hierarchical POMDPs for Planning Information Processing and Sensing Actions on a Robot

Flexible general purpose robots need to tailor their visual processing to their task, on the fly. We propose a new approach to this within a planning framework, where the goal is to plan a sequence of visual operators to apply to the regions of interest (ROIs) in a scene. We pose the visual processing problem as a Partially Observable Markov Decision Process (POMDP). This requires probabilistic models of operator effects to quantitatively capture the unreliability of the processing actions, and thus reason precisely about trade-offs between plan execution time and plan reliability. Since planning in practical sized POMDPs is intractable we show how to ameliorate this intractability somewhat for our domain by defining a hierarchical POMDP. We compare the hierarchical POMDP approach with a Continual Planning (CP) approach. On a real robot visual domain, we show empirically that all the planning methods outperform naive application of all visual operators. The key result is that the POMD...

• Type: Conference
• Year: 2008
• Where: AIPS
• Authors: Mohan Sridharan, Jeremy L. Wyatt, Richard Dearden
• Keywords: AIPS 2008, Artificial Intelligence, Visual Operators, Visual Processing, Visual Processing Problems